摘要
针对多维非高斯系统提出了最小熵控制方法,控制的目标是使系统的非高斯输出概率密度函数跟踪一个已知的联合概率密度函数.首先,根据系统模型和辅助映射,构建了系统状态、跟踪误差与扰动输入之间的泛函算子模型,然后基于梯度算法设计了递归的次优控制律,最后通过仿真验证了最小熵控制算法的有效性.
This paper considers a new tracking control problem for a class of multivariate non-Gaussian system where the tracked target is a given joint probability density function. The control objective is to find an algorithm such that the entropy of tracking error is minimized. A function based model is established between the disturbance, state and error. A recursive optimization approach is presented such that the performance index is minimized. Final- ly, simulations are provided to demonstrate the effectiveness of the proposed minimum entroov control algorithm.
出处
《南京信息工程大学学报（自然科学版）》
CAS
2012年第6期545-549,共5页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家自然科学基金(61104123
61104073)
博士后基金(2012M520141)
南京信息工程大学科研启动基金(S8110123001)
关键词
非高斯系统
最小熵
概率密度函数(PDF)
跟踪控制
泛函算子模型
non-Gaussian systems
minimum entropy
Probability Density Functions （PDF）
tracking control
func-tional operator model
